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[1] "/Users/swvanderlaan/git/CirculatoryHealth/AE_20211201_YAW_SWVANDERLAAN_HDAC9"
[1] "_archived" "1. AEDB.CEA.baseline.nb.html" "1. AEDB.CEA.baseline.Rmd"
[4] "2. SNP_analyses.Rmd" "20220319.HDAC9.AEDB.CEA.baseline.RData" "20220319.HDAC9.bulkRNAseq.main_analysis.RData"
[7] "20220319.HDAC9.bulkRNAseq.preparation.RData" "3.1 bulkRNAseq.preparation.nb.html" "3.1 bulkRNAseq.preparation.Rmd"
[10] "3.2 bulkRNAseq.main_analysis.nb.html" "3.2 bulkRNAseq.main_analysis.Rmd" "3.3 bulkRNAseq.additional_figures.Rmd"
[13] "4. scRNAseq.Rmd" "AE_20211201_YAW_SWVANDERLAAN_HDAC9.Rproj" "AnalysisPlan"
[16] "HDAC9" "images" "LICENSE"
[19] "README.html" "README.md" "references.bib"
[22] "renv" "renv.lock" "scripts"
[25] "SNP" "targets"
source(paste0(PROJECT_loc, "/scripts/functions.R"))ggplot2::theme_set(ggplot2::theme_minimal())
pander::panderOptions("table.split.table", Inf)install.packages.auto("pander")
install.packages.auto("readr")
install.packages.auto("optparse")
install.packages.auto("tools")
install.packages.auto("dplyr")
install.packages.auto("tidyr")
install.packages.auto("naniar")
# To get 'data.table' with 'fwrite' to be able to directly write gzipped-files
# Ref: https://stackoverflow.com/questions/42788401/is-possible-to-use-fwrite-from-data-table-with-gzfile
# install.packages("data.table", repos = "https://Rdatatable.gitlab.io/data.table")
library(data.table)
install.packages.auto("tidyverse")
install.packages.auto("knitr")
install.packages.auto("DT")
install.packages.auto("eeptools")
install.packages.auto("openxlsx")
install.packages.auto("haven")
install.packages.auto("tableone")
install.packages.auto("sjPlot")
install.packages.auto("BlandAltmanLeh")
# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')
# for plotting
install.packages.auto("pheatmap")
install.packages.auto("forestplot")
install.packages.auto("ggplot2")
install.packages.auto("ggpubr")
install.packages.auto("UpSetR")
devtools::install_github("thomasp85/patchwork")Using github PAT from envvar GITHUB_PAT
Skipping install of 'patchwork' from a github remote, the SHA1 (79223d30) has not changed since last install.
Use `force = TRUE` to force installation
# for Seurat etc
install.packages.auto("org.Hs.eg.db")
install.packages.auto("mygene")
install.packages.auto("EnhancedVolcano")
# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')
# Replace '2.3.4' with your desired version
# devtools::install_version(package = 'Seurat', version = package_version('2.3.4'))
# install.packages("Seurat")
install.packages.auto("Seurat") # latest versionLoading required package: Seurat
Attaching SeuratObject
Attaching package: 'Seurat'
The following object is masked from 'package:Hmisc':
Key
The following object is masked from 'package:SummarizedExperiment':
Assays
The following object is masked from 'package:DT':
JS
library("Seurat")
Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")
### UtrechtScienceParkColoursScheme
###
### WebsitetoconvertHEXtoRGB:http://hex.colorrrs.com.
### Forsomefunctionsyoushoulddividethesenumbersby255.
###
### No. Color HEX (RGB) CHR MAF/INFO
###---------------------------------------------------------------------------------------
### 1 yellow #FBB820 (251,184,32) => 1 or 1.0>INFO
### 2 gold #F59D10 (245,157,16) => 2
### 3 salmon #E55738 (229,87,56) => 3 or 0.05<MAF<0.2 or 0.4<INFO<0.6
### 4 darkpink #DB003F ((219,0,63) => 4
### 5 lightpink #E35493 (227,84,147) => 5 or 0.8<INFO<1.0
### 6 pink #D5267B (213,38,123) => 6
### 7 hardpink #CC0071 (204,0,113) => 7
### 8 lightpurple #A8448A (168,68,138) => 8
### 9 purple #9A3480 (154,52,128) => 9
### 10 lavendel #8D5B9A (141,91,154) => 10
### 11 bluepurple #705296 (112,82,150) => 11
### 12 purpleblue #686AA9 (104,106,169) => 12
### 13 lightpurpleblue #6173AD (97,115,173/101,120,180) => 13
### 14 seablue #4C81BF (76,129,191) => 14
### 15 skyblue #2F8BC9 (47,139,201) => 15
### 16 azurblue #1290D9 (18,144,217) => 16 or 0.01<MAF<0.05 or 0.2<INFO<0.4
### 17 lightazurblue #1396D8 (19,150,216) => 17
### 18 greenblue #15A6C1 (21,166,193) => 18
### 19 seaweedgreen #5EB17F (94,177,127) => 19
### 20 yellowgreen #86B833 (134,184,51) => 20
### 21 lightmossgreen #C5D220 (197,210,32) => 21
### 22 mossgreen #9FC228 (159,194,40) => 22 or MAF>0.20 or 0.6<INFO<0.8
### 23 lightgreen #78B113 (120,177,19) => 23/X
### 24 green #49A01D (73,160,29) => 24/Y
### 25 grey #595A5C (89,90,92) => 25/XY or MAF<0.01 or 0.0<INFO<0.2
### 26 lightgrey #A2A3A4 (162,163,164) => 26/MT
###
### ADDITIONAL COLORS
### 27 midgrey #D7D8D7
### 28 verylightgrey #ECECEC"
### 29 white #FFFFFF
### 30 black #000000
###----------------------------------------------------------------------------------------------
uithof_color = c("#FBB820","#F59D10","#E55738","#DB003F","#E35493","#D5267B",
"#CC0071","#A8448A","#9A3480","#8D5B9A","#705296","#686AA9",
"#6173AD","#4C81BF","#2F8BC9","#1290D9","#1396D8","#15A6C1",
"#5EB17F","#86B833","#C5D220","#9FC228","#78B113","#49A01D",
"#595A5C","#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")
uithof_color_legend = c("#FBB820", "#F59D10", "#E55738", "#DB003F", "#E35493",
"#D5267B", "#CC0071", "#A8448A", "#9A3480", "#8D5B9A",
"#705296", "#686AA9", "#6173AD", "#4C81BF", "#2F8BC9",
"#1290D9", "#1396D8", "#15A6C1", "#5EB17F", "#86B833",
"#C5D220", "#9FC228", "#78B113", "#49A01D", "#595A5C",
"#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")
### ----------------------------------------------------------------------------For the ERA-CVD ‘druggable-MI-targets’ project (grantnumber: 01KL1802) we performed two related RNA sequencing (RNAseq) experiments:
conventional (‘bulk’) RNAseq using RNA extracted from carotid plaque samples, n ± 700. As of Saturday, March 19, 2022 all samples have been selected and RNA has been extracted; quality control (QC) was performed and we have a dataset of 635 samples.
single-cell RNAseq (scRNAseq) of at least n = 40 samples (20 females, 20 males). As of Saturday, March 19, 2022 data is available of 40 samples (3 females, 15 males), we are extending sampling to get more female samples.
Plaque samples are derived from carotid endarterectomies as part of the Athero-Express Biobank Study which is an ongoing study in the UMC Utrecht.
Here we map the HDAC9 to single-cells from the plaques.
library(openxlsx)
gene_list_df <- read.xlsx(paste0(PROJECT_loc, "/targets/Genes.xlsx"), sheet = "Genes")
target_genes <- unlist(gene_list_df$Gene)
target_genes[1] "HDAC9" "TWIST1" "IL6" "IL1B"
First we will load the data:
Here we load the latest dataset from our Athero-Express single-cell RNA experiment.
# load(paste0(AESCRNA_loc, "/20210811.46.patients.KP.RData"))
# scRNAseqData <- seuset
# rm(seuset)
#
# saveRDS(scRNAseqData, paste0(AESCRNA_loc, "/20210811.46.patients.KP.RDS"))
scRNAseqData <- readRDS(paste0(AESCRNA_loc, "/20210811.46.patients.KP.RDS"))
scRNAseqDataAn object of class Seurat
36147 features across 4948 samples within 2 assays
Active assay: RNA (20111 features, 0 variable features)
1 other assay present: SCT
2 dimensional reductions calculated: pca, umap
The naming/classification is based on a combination conventional markers. We do not claim to know the exact identity of each cell, rather we refer to cells as ‘KIT+ Mast cells”-like cells. Likewise we refer to the cell clusters as ’communities’ of cells that exhibit similar properties, i.e. similar defining markers (e.g. KIT).
We will rename the cell types to human readable names.
### change names for clarity
backup.scRNAseqData = scRNAseqData
# get the old names to change to new names
UMAPPlot(scRNAseqData, label = FALSE, pt.size = 1.25, label.size = 4, group.by = "ident")unique(scRNAseqData@active.ident) [1] CD3+ T Cells I CD3+ T Cells IV CD34+ Endothelial Cells I
[4] CD3+ T Cells V CD3+CD56+ NK Cells II CD3+ T Cells VI
[7] CD68+IL18+TLR4+TREM2+ Resident macrophages CD3+CD56+ NK Cells I ACTA2+ Smooth Muscle Cells
[10] CD3+ T Cells II FOXP3+ T Cells CD34+ Endothelial Cells II
[13] CD3+ T Cells III CD68+CD1C+ Dendritic Cells CD68+CASP1+IL1B+SELL+ Inflammatory macrophages
[16] CD79A+ Class-switched Memory B Cells CD68+ABCA1+OLR1+TREM2+ Foam Cells CD68+KIT+ Mast Cells
[19] CD68+CD4+ Monocytes CD79+ Plasma B Cells
20 Levels: CD3+ T Cells I CD3+ T Cells II CD3+ T Cells III CD3+ T Cells IV CD68+IL18+TLR4+TREM2+ Resident macrophages ... CD79+ Plasma B Cells
celltypes <- c("CD68+CD4+ Monocytes" = "CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ Resident macrophages" = "CD68+IL18+TLR4+TREM2+ MRes",
"CD68+CD1C+ Dendritic Cells" = "CD68+CD1C+ DC",
"CD68+CASP1+IL1B+SELL+ Inflammatory macrophages" = "CD68+CASP1+IL1B+SELL MInf",
"CD68+ABCA1+OLR1+TREM2+ Foam Cells" = "CD68+ABCA1+OLR1+TREM2+ FC",
# T-cells
"CD3+ T Cells I" = "CD3+ TC I",
"CD3+ T Cells II" = "CD3+ TC II",
"CD3+ T Cells III" = "CD3+ TC III",
"CD3+ T Cells IV" = "CD3+ TC IV",
"CD3+ T Cells V" = "CD3+ TC V",
"CD3+ T Cells VI" = "CD3+ TC VI",
"FOXP3+ T Cells" = "FOXP3+ TC",
# Endothelial cells
"CD34+ Endothelial Cells I" = "CD34+ EC I",
"CD34+ Endothelial Cells II" = "CD34+ EC II",
# SMC
"ACTA2+ Smooth Muscle Cells" = "ACTA2+ SMC",
# NK Cells
"CD3+CD56+ NK Cells I" = "CD3+CD56+ NK I",
"CD3+CD56+ NK Cells II" = "CD3+CD56+ NK II",
# Mast
"CD68+KIT+ Mast Cells" = "CD68+KIT+ MC",
"CD79A+ Class-switched Memory B Cells" = "CD79A+ BCmem",
"CD79+ Plasma B Cells" = "CD79+ BCplasma")
scRNAseqData <- Seurat::RenameIdents(object = scRNAseqData,
celltypes)UMAPPlot(scRNAseqData, label = TRUE, pt.size = 1.25, label.size = 4, group.by = "ident",
repel = TRUE)Loading the Athero-Express clinical data.
AEDB.CEA <- readRDS(file = paste0(OUT_loc, "/20220319.",TRAIT_OF_INTEREST,".AEDB.CEA.RDS"))
# Baseline table variables
basetable_vars = c("Hospital", "ORyear", "Artery_summary",
"Age", "Gender",
# "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU",
"TC_final", "LDL_final", "HDL_final", "TG_final",
# "hsCRP_plasma",
"systolic", "diastoli", "GFR_MDRD", "BMI",
"KDOQI", "BMI_WHO",
"SmokerStatus", "AlcoholUse",
"DiabetesStatus",
"Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
"Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
"Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
"Symptoms.Update2G", "Symptoms.Update3G", "indexsymptoms_latest_4g",
"restenos", "stenose",
"CAD_history", "PAOD", "Peripheral.interv",
"EP_composite", "EP_composite_time", "EP_major", "EP_major_time",
"MAC_rankNorm", "SMC_rankNorm", "Macrophages.bin", "SMC.bin",
"Neutrophils_rankNorm", "MastCells_rankNorm",
"IPH.bin", "VesselDensity_rankNorm",
"Calc.bin", "Collagen.bin",
"Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype", "Plaque_Vulnerability_Index")
basetable_bin = c("Gender", "Artery_summary",
"KDOQI", "BMI_WHO",
"SmokerStatus", "AlcoholUse",
"DiabetesStatus",
"Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
"Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
"Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
"Symptoms.Update2G", "Symptoms.Update3G", "indexsymptoms_latest_4g",
"restenos", "stenose",
"CAD_history", "PAOD", "Peripheral.interv",
"EP_major", "EP_composite", "Macrophages.bin", "SMC.bin",
"IPH.bin",
"Calc.bin", "Collagen.bin",
"Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype", "Plaque_Vulnerability_Index")
# basetable_bin
basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_conmetadata <- scRNAseqData@meta.data %>% as_tibble() %>% separate(orig.ident, c("Patient", NA))
scRNAseqDataMeta <- metadata %>% distinct(Patient, .keep_all = TRUE)
scRNAseqDataMetaAE <- merge(scRNAseqDataMeta, AEDB.CEA, by.x = "Patient", by.y = "STUDY_NUMBER", sort = FALSE, all.x = TRUE)
dim(scRNAseqDataMetaAE)[1] 46 1144
# Replace missing data
# Ref: https://cran.r-project.org/web/packages/naniar/vignettes/replace-with-na.html
require(naniar)
na_strings <- c("NA", "N A", "N / A", "N/A", "N/ A",
"Not Available", "Not available",
"missing",
"-999", "-99",
"No data available/missing", "No data available/Missing")
# Then you write ~.x %in% na_strings - which reads as “does this value occur in the list of NA strings”.
scRNAseqDataMetaAE %>%
replace_with_na_all(condition = ~.x %in% na_strings)cat("====================================================================================================")====================================================================================================
cat("SELECTION THE SHIZZLE")SELECTION THE SHIZZLE
cat("- sanity checking PRIOR to selection")- sanity checking PRIOR to selection
library(data.table)
require(labelled)
ae.gender <- to_factor(scRNAseqDataMetaAE$Gender)
ae.hospital <- to_factor(scRNAseqDataMetaAE$Hospital)
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"), useNA = "ifany") Hospital
Sex St. Antonius, Nieuwegein UMC Utrecht <NA>
female 0 10 0
male 0 26 0
<NA> 0 0 10
ae.artery <- to_factor(scRNAseqDataMetaAE$Artery_summary)
table(ae.artery, ae.gender, dnn = c("Sex", "Artery"), useNA = "ifany") Artery
Sex female male <NA>
No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA 0 0 0
carotid (left & right) 10 25 0
femoral/iliac (left, right or both sides) 0 0 0
other carotid arteries (common, external) 0 1 0
carotid bypass and injury (left, right or both sides) 0 0 0
aneurysmata (carotid & femoral) 0 0 0
aorta 0 0 0
other arteries (renal, popliteal, vertebral) 0 0 0
femoral bypass, angioseal and injury (left, right or both sides) 0 0 0
<NA> 0 0 10
ae.ic <- to_factor(scRNAseqDataMetaAE$informedconsent)
table(ae.ic, ae.gender, useNA = "ifany") ae.gender
ae.ic female male <NA>
missing 0 0 0
no, died 0 0 0
yes 6 14 0
yes, health treatment when possible 2 7 0
yes, no health treatment 1 2 0
yes, no health treatment, no commercial business 1 2 0
yes, no tissue, no commerical business 0 0 0
yes, no tissue, no questionnaires, no medical info, no commercial business 0 0 0
yes, no questionnaires, no health treatment, no commercial business 0 0 0
yes, no questionnaires, health treatment when possible 0 0 0
yes, no tissue, no questionnaires, no health treatment, no commerical business 0 0 0
yes, no health treatment, no medical info, no commercial business 0 0 0
yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business 0 0 0
yes, no questionnaires, no health treatment 0 0 0
yes, no tissue, no health treatment 0 0 0
yes, no tissue, no questionnaires 0 0 0
yes, no tissue, health treatment when possible 0 0 0
yes, no tissue 0 0 0
yes, no commerical business 0 1 0
yes, health treatment when possible, no commercial business 0 0 0
yes, no medical info, no commercial business 0 0 0
yes, no questionnaires 0 0 0
yes, no tissue, no questionnaires, no health treatment, no medical info 0 0 0
yes, no tissue, no questionnaires, no health treatment, no commercial business 0 0 0
yes, no medical info 0 0 0
yes, no questionnaires, no commercial business 0 0 0
yes, no questionnaires, no health treatment, no medical info 0 0 0
yes, no questionnaires, health treatment when possible, no commercial business 0 0 0
yes, no health treatment, no medical info 0 0 0
no, doesn't want to 0 0 0
no, unable to sign 0 0 0
no, no reaction 0 0 0
no, lost 0 0 0
no, too old 0 0 0
yes, no medical info, health treatment when possible 0 0 0
no (never asked for IC because there was no tissue) 0 0 0
yes, no medical info, no commercial business, health treatment when possible 0 0 0
no, endpoint 0 0 0
wil niets invullen, wel alles gebruiken 0 0 0
second informed concents: yes, no commercial business 0 0 0
nooit geincludeerd 0 0 0
<NA> 0 0 10
rm(ae.gender, ae.hospital, ae.artery, ae.ic)
scRNAseqDataMetaAE.all <- subset(scRNAseqDataMetaAE,
(Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)" ) & # we only want carotids
informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
informedconsent != "no, died" &
informedconsent != "yes, no tissue, no commerical business" &
informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
informedconsent != "yes, no tissue, no health treatment" &
informedconsent != "yes, no tissue, no questionnaires" &
informedconsent != "yes, no tissue, health treatment when possible" &
informedconsent != "yes, no tissue" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
informedconsent != "no, doesn't want to" &
informedconsent != "no, unable to sign" &
informedconsent != "no, no reaction" &
informedconsent != "no, lost" &
informedconsent != "no, too old" &
informedconsent != "yes, no medical info, health treatment when possible" &
informedconsent != "no (never asked for IC because there was no tissue)" &
informedconsent != "no, endpoint" &
informedconsent != "nooit geincludeerd" &
informedconsent != "yes, no health treatment, no commercial business" & # IMPORTANT: since we are sharing with a commercial party
informedconsent != "yes, no tissue, no commerical business" &
informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
informedconsent != "yes, no questionnaires, no health treatment, no commercial business" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
informedconsent != "yes, no health treatment, no medical info, no commercial business" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
informedconsent != "yes, no commerical business" &
informedconsent != "yes, health treatment when possible, no commercial business" &
informedconsent != "yes, no medical info, no commercial business" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
informedconsent != "yes, no questionnaires, no commercial business" &
informedconsent != "yes, no questionnaires, health treatment when possible, no commercial business" &
informedconsent != "second informed concents: yes, no commercial business")
# scRNAseqDataMetaAE.all[1:10, 1:10]
dim(scRNAseqDataMetaAE.all)[1] 32 1144
# DT::datatable(scRNAseqDataMetaAE.all)Showing the baseline table for the scRNAseq data in 39 CEA patients with informed consent.
cat("===========================================================================================")===========================================================================================
cat("CREATE BASELINE TABLE")CREATE BASELINE TABLE
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
scRNAseqDataMetaAE.all.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
# strata = "Gender",
data = scRNAseqDataMetaAE.all, includeNA = TRUE),
nonnormal = c(),
quote = FALSE, showAllLevels = TRUE,
format = "p",
contDigits = 3)[,1:2]Warning in ModuleReturnVarsExist(vars, data) :
These variables only have NA/NaN: MAC_rankNorm SMC_rankNorm Macrophages.bin SMC.bin Neutrophils_rankNorm MastCells_rankNorm IPH.bin VesselDensity_rankNorm Calc.bin Collagen.bin Fat.bin_10 Fat.bin_40 OverallPlaquePhenotype Dropped
level Overall
n 32
Hospital (%) St. Antonius, Nieuwegein 0.0
UMC Utrecht 100.0
ORyear (%) No data available/missing 0.0
2002 0.0
2003 0.0
2004 0.0
2005 0.0
2006 0.0
2007 0.0
2008 0.0
2009 0.0
2010 0.0
2011 0.0
2012 0.0
2013 0.0
2014 0.0
2015 0.0
2016 0.0
2017 0.0
2018 62.5
2019 37.5
Artery_summary (%) No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA 0.0
carotid (left & right) 96.9
femoral/iliac (left, right or both sides) 0.0
other carotid arteries (common, external) 3.1
carotid bypass and injury (left, right or both sides) 0.0
aneurysmata (carotid & femoral) 0.0
aorta 0.0
other arteries (renal, popliteal, vertebral) 0.0
femoral bypass, angioseal and injury (left, right or both sides) 0.0
Age (mean (SD)) 72.156 (8.651)
Gender (%) female 28.1
male 71.9
TC_final (mean (SD)) 4.467 (1.261)
LDL_final (mean (SD)) 2.659 (1.008)
HDL_final (mean (SD)) 1.120 (0.238)
TG_final (mean (SD)) 1.892 (1.124)
systolic (mean (SD)) 152.516 (27.422)
diastoli (mean (SD)) 80.452 (17.171)
GFR_MDRD (mean (SD)) 82.510 (32.197)
BMI (mean (SD)) 26.458 (3.556)
KDOQI (%) No data available/missing 0.0
Normal kidney function 31.2
CKD 2 (Mild) 34.4
CKD 3 (Moderate) 25.0
CKD 4 (Severe) 0.0
CKD 5 (Failure) 0.0
<NA> 9.4
BMI_WHO (%) No data available/missing 0.0
Underweight 3.1
Normal 31.2
Overweight 40.6
Obese 15.6
<NA> 9.4
SmokerStatus (%) Current smoker 34.4
Ex-smoker 46.9
Never smoked 12.5
<NA> 6.2
AlcoholUse (%) No 34.4
Yes 56.2
<NA> 9.4
DiabetesStatus (%) Control (no Diabetes Dx/Med) 65.6
Diabetes 31.2
<NA> 3.1
Hypertension.selfreport (%) No data available/missing 0.0
no 9.4
yes 84.4
<NA> 6.2
Hypertension.selfreportdrug (%) No data available/missing 0.0
no 9.4
yes 84.4
<NA> 6.2
Hypertension.composite (%) No data available/missing 0.0
no 6.2
yes 90.6
<NA> 3.1
Hypertension.drugs (%) No data available/missing 0.0
no 6.2
yes 87.5
<NA> 6.2
Med.anticoagulants (%) No data available/missing 0.0
no 84.4
yes 6.2
<NA> 9.4
Med.all.antiplatelet (%) No data available/missing 0.0
no 25.0
yes 68.8
<NA> 6.2
Med.Statin.LLD (%) No data available/missing 0.0
no 21.9
yes 71.9
<NA> 6.2
Stroke_Dx (%) Missing 0.0
No stroke diagnosed 50.0
Stroke diagnosed 46.9
<NA> 3.1
sympt (%) missing 0.0
Asymptomatic 18.8
TIA 15.6
minor stroke 31.2
Major stroke 6.2
Amaurosis fugax 15.6
Four vessel disease 0.0
Vertebrobasilary TIA 0.0
Retinal infarction 3.1
Symptomatic, but aspecific symtoms 0.0
Contralateral symptomatic occlusion 0.0
retinal infarction 3.1
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0
retinal infarction + TIAs 0.0
Ocular ischemic syndrome 6.2
ischemisch glaucoom 0.0
subclavian steal syndrome 0.0
TGA 0.0
Symptoms.5G (%) Asymptomatic 18.8
Ocular 21.9
Other 0.0
Retinal infarction 6.2
Stroke 37.5
TIA 15.6
AsymptSympt (%) Asymptomatic 18.8
Ocular and others 28.1
Symptomatic 53.1
AsymptSympt2G (%) Asymptomatic 18.8
Symptomatic 81.2
Symptoms.Update2G (%) Asymptomatic 18.8
Symptomatic 81.2
Symptoms.Update3G (%) Asymptomatic 18.8
Symptomatic 81.2
Unclear 0.0
indexsymptoms_latest_4g (mean (SD)) 1.719 (1.170)
restenos (%) missing 0.0
de novo 100.0
restenosis 0.0
stenose bij angioseal na PTCA 0.0
stenose (%) missing 0.0
0-49% 3.1
50-70% 12.5
70-90% 43.8
90-99% 21.9
100% (Occlusion) 0.0
NA 0.0
50-99% 0.0
70-99% 18.8
99 0.0
CAD_history (%) Missing 0.0
No history CAD 78.1
History CAD 18.8
<NA> 3.1
PAOD (%) missing/no data 0.0
no 81.2
yes 15.6
<NA> 3.1
Peripheral.interv (%) no 78.1
yes 18.8
<NA> 3.1
EP_composite (%) No data available. 0.0
No composite endpoints 37.5
Composite endpoints 12.5
<NA> 50.0
EP_composite_time (mean (SD)) 0.905 (0.522)
EP_major (%) No data available. 0.0
No major events (endpoints) 40.6
Major events (endpoints) 9.4
<NA> 50.0
EP_major_time (mean (SD)) 0.905 (0.522)
Plaque_Vulnerability_Index (%) 0 100.0
1 0.0
2 0.0
3 0.0
4 0.0
5 0.0
Writing the baseline table to Excel format.
# Write basetable
require(openxlsx)
# write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AESCRNA.CEA.39pts.after_qc.IC_commercial.BaselineTable.xlsx"),
# format(scRNAseqDataMetaAE.all.tableOne, digits = 5, scientific = FALSE) ,
# rowNames = TRUE, colNames = TRUE, overwrite = TRUE)
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AESCRNA.CEA.32pts.after_qc.IC_academic.BaselineTable.xlsx"),
format(scRNAseqDataMetaAE.all.tableOne, digits = 5, scientific = FALSE) ,
rowNames = TRUE, colNames = TRUE, overwrite = TRUE)Here review the number of cells per sample, plate, and patients. And plot the ratio’s per sample and study number.
## check stuff
cat("\nHow many cells per type ...?")
How many cells per type ...?
sort(table(scRNAseqData@meta.data$SCT_snn_res.0.8))integer(0)
# cat("\n\nHow many cells per plate ...?")
# sort(table(scRNAseqData@meta.data$ID))
# cat("\n\nHow many cells per type per plate ...?")
# table(scRNAseqData@meta.data$SCT_snn_res.0.8, scRNAseqData@meta.data$ID)
cat("\n\nHow many cells per patient ...?")
How many cells per patient ...?
sort(table(scRNAseqData@meta.data$Patient))
4530 4675 4440 4605 4653 4472 4458 4455 4476 4587 4496 4601 4502 4501 4571 4478 4448 4477 4452 4459 4520 4602 4489 4432 4495 4545 4558 4480 4447 4500 4513 4535
3 4 6 7 20 22 35 54 59 60 70 70 73 75 76 77 80 84 92 94 96 96 97 99 102 106 107 112 114 116 123 130
4676 4486 4470 4487 4546 4488 4521 4580 4491 4541 4450 4542 4453 4443
135 137 144 144 144 146 161 163 175 178 205 213 222 422
cat("\n\nVisualizing these ratio's per study number and sample ...?")
Visualizing these ratio's per study number and sample ...?
UMAPPlot(scRNAseqData, label = TRUE, pt.size = 1.25, label.size = 4, group.by = "ident",
repel = TRUE)ggsave(paste0(PLOT_loc, "/", Today, ".UMAP.png"), plot = last_plot())Saving 18 x 12 in image
ggsave(paste0(PLOT_loc, "/", Today, ".UMAP.ps"), plot = last_plot())Saving 18 x 12 in image
# barplot(prop.table(x = table(scRNAseqData@active.ident, scRNAseqData@meta.data$Patient)),
# cex.axis = 1.0, cex.names = 0.5, las = 1,
# col = uithof_color, xlab = "study number", legend.text = FALSE, args.legend = list(x = "bottom"))
# dev.copy(pdf, paste0(QC_loc, "/", Today, ".cell_ratios_per_sample.pdf"))
# dev.off()
# barplot(prop.table(x = table(scRNAseqData@active.ident, scRNAseqData@meta.data$ID)),
# cex.axis = 1.0, cex.names = 0.5, las = 2,
# col = uithof_color, xlab = "sample ID", legend.text = FALSE, args.legend = list(x = "bottom"))
# dev.copy(pdf, paste0(QC_loc, "/", Today, ".cell_ratios_per_sample_per_plate.pdf"))
# dev.off()Let’s project known cellular markers.
UMAPPlot(scRNAseqData, label = FALSE, pt.size = 1.25, label.size = 4, group.by = "ident",
repel = TRUE)
# endothelial cells
FeaturePlot(scRNAseqData, features = c("CD34"), cols = c("#ECECEC", "#DB003F"))FeaturePlot(scRNAseqData, features = c("EDN1"), cols = c("#ECECEC", "#DB003F"))FeaturePlot(scRNAseqData, features = c("EDNRA", "EDNRB"), cols = c("#ECECEC", "#DB003F"))FeaturePlot(scRNAseqData, features = c("CDH5", "PECAM1"), cols = c("#ECECEC", "#DB003F"))FeaturePlot(scRNAseqData, features = c("ACKR1"), cols = c("#ECECEC", "#DB003F"))
# SMC
FeaturePlot(scRNAseqData, features = c("MYH11"), cols = c("#ECECEC", "#DB003F"))FeaturePlot(scRNAseqData, features = c("LGALS3", "ACTA2"), cols = c("#ECECEC", "#DB003F"))
# macrophages
FeaturePlot(scRNAseqData, features = c("CD14", "CD68"), cols = c("#ECECEC", "#DB003F"))FeaturePlot(scRNAseqData, features = c("CD36"), cols = c("#ECECEC", "#DB003F"))
# t-cells
FeaturePlot(scRNAseqData, features = c("CD3E"), cols = c("#ECECEC", "#DB003F"))FeaturePlot(scRNAseqData, features = c("CD4"), cols = c("#ECECEC", "#DB003F"))# FeaturePlot(scRNAseqData, features = c("CD8"), cols = c("#ECECEC", "#DB003F"))
# b-cells
FeaturePlot(scRNAseqData, features = c("CD79A"), cols = c("#ECECEC", "#DB003F"))
# mast cells
FeaturePlot(scRNAseqData, features = c("KIT"), cols = c("#ECECEC", "#DB003F"))
# NK cells
FeaturePlot(scRNAseqData, features = c("NCAM1"), cols = c("#ECECEC", "#DB003F"))We check whether the targets genes were sequenced using our method.
length(target_genes)[1] 4
target_genes[1] "HDAC9" "TWIST1" "IL6" "IL1B"
# target_genes_rm <- c("AC011294.3", "C6orf195", "C9orf53", "AL137026.1", "RP11-145E5.5",
# "ZNF32", "BCAM", "DUPD1", "PVRL2")
#
# temp = target_genes[!target_genes %in% target_genes_rm]
#
# target_genes_qc <- c(temp, "DUSP27", "NECTIN2")
target_genes_qc <- target_genes
target_genes_qc[1] "HDAC9" "TWIST1" "IL6" "IL1B"
library(RColorBrewer)
p1 <- DotPlot(scRNAseqData, features = target_genes_qc,
cols = "RdBu")
p1 + theme(axis.text.x = element_text(angle = 45, hjust=1, size = 5))
ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.png"), plot = last_plot())Saving 18 x 12 in image
ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.ps"), plot = last_plot())Saving 18 x 12 in image
ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.pdf"), plot = last_plot())Saving 18 x 12 in image
rm(p1)
# FeaturePlot(scRNAseqData, features = c(target_genes_qc),
# cols = c("#ECECEC", "#DB003F", "#9A3480","#1290D9"),
# combine = TRUE)
#
# ggsave(paste0(PLOT_loc, "/", Today, ".FeaturePlot.Targets.png"), plot = last_plot())
# ggsave(paste0(PLOT_loc, "/", Today, ".FeaturePlot.Targets.ps"), plot = last_plot())# VlnPlot(scRNAseqData, features = "DUSP27")
# VlnPlot files
ifelse(!dir.exists(file.path(PLOT_loc, "/VlnPlot")),
dir.create(file.path(PLOT_loc, "/VlnPlot")),
FALSE)[1] TRUE
VlnPlot_loc = paste0(PLOT_loc, "/VlnPlot")
for (GENE in target_genes_qc){
print(paste0("Projecting the expression of ", GENE, "."))
vp1 <- VlnPlot(scRNAseqData, features = GENE) +
xlab("cell communities") +
ylab(bquote("normalized expression")) +
theme(axis.title.x = element_text(color = "#000000", size = 14, face = "bold"),
axis.title.y = element_text(color = "#000000", size = 14, face = "bold"),
legend.position = "none")
ggsave(paste0(VlnPlot_loc, "/", Today, ".VlnPlot.",GENE,".png"), plot = last_plot())
ggsave(paste0(VlnPlot_loc, "/", Today, ".VlnPlot.",GENE,".ps"), plot = last_plot())
ggsave(paste0(VlnPlot_loc, "/", Today, ".VlnPlot.",GENE,".pdf"), plot = last_plot())
# print(vp1)
}[1] "Projecting the expression of HDAC9."
Saving 7 x 7 in image
Saving 7 x 7 in image
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[1] "Projecting the expression of TWIST1."
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[1] "Projecting the expression of IL6."
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[1] "Projecting the expression of IL1B."
Saving 7 x 7 in image
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Here we project genes to only the broad cell communities:
unique(scRNAseqData@active.ident) [1] CD3+ TC I CD3+ TC IV CD34+ EC I CD3+ TC V CD3+CD56+ NK II
[6] CD3+ TC VI CD68+IL18+TLR4+TREM2+ MRes CD3+CD56+ NK I ACTA2+ SMC CD3+ TC II
[11] FOXP3+ TC CD34+ EC II CD3+ TC III CD68+CD1C+ DC CD68+CASP1+IL1B+SELL MInf
[16] CD79A+ BCmem CD68+ABCA1+OLR1+TREM2+ FC CD68+KIT+ MC CD68+CD4+ Mono CD79+ BCplasma
20 Levels: CD68+CD4+ Mono CD68+IL18+TLR4+TREM2+ MRes CD68+CD1C+ DC CD68+CASP1+IL1B+SELL MInf CD68+ABCA1+OLR1+TREM2+ FC CD3+ TC I CD3+ TC II ... CD79+ BCplasma
Comparison between the macrophages cell communities (CD14/CD68+), and all other communities.
MAC.markers <- FindMarkers(object = scRNAseqData,
ident.1 = c("CD68+CASP1+IL1B+SELL MInf",
"CD68+CD1C+ DC",
"CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ MRes",
"CD68+ABCA1+OLR1+TREM2+ FC"),
ident.2 = c(#"CD68+CASP1+IL1B+SELL MInf",
#"CD68+CD1C+ DC",
#"CD68+CD4+ Mono",
#"CD68+IL18+TLR4+TREM2+ MRes",
#"CD68+ABCA1+OLR1+TREM2+ FC",
"CD3+ TC I",
"CD3+ TC II",
"CD3+ TC III",
"CD3+ TC IV",
"CD3+ TC V",
"CD3+ TC VI",
"FOXP3+ TC",
"CD34+ EC I",
"CD34+ EC II",
"ACTA2+ SMC",
"CD3+CD56+ NK I",
"CD3+CD56+ NK II",
"CD68+KIT+ MC",
"CD79+ BCplasma",
"CD79A+ BCmem"))
| | 0 % ~calculating
|+ | 1 % ~01m 05s
|++ | 2 % ~01m 02s
|++ | 3 % ~01m 02s
|+++ | 4 % ~01m 03s
|+++ | 5 % ~01m 04s
|++++ | 6 % ~01m 03s
|++++ | 7 % ~01m 02s
|+++++ | 8 % ~01m 01s
|+++++ | 9 % ~01m 02s
|++++++ | 10% ~01m 01s
|++++++ | 11% ~60s
|+++++++ | 12% ~59s
|+++++++ | 13% ~58s
|++++++++ | 14% ~58s
|++++++++ | 15% ~57s
|+++++++++ | 16% ~56s
|+++++++++ | 17% ~56s
|++++++++++ | 18% ~55s
|++++++++++ | 19% ~54s
|+++++++++++ | 20% ~53s
|+++++++++++ | 21% ~53s
|++++++++++++ | 22% ~53s
|++++++++++++ | 23% ~52s
|+++++++++++++ | 24% ~51s
|+++++++++++++ | 25% ~50s
|++++++++++++++ | 26% ~50s
|++++++++++++++ | 27% ~49s
|+++++++++++++++ | 28% ~48s
|+++++++++++++++ | 29% ~48s
|++++++++++++++++ | 30% ~47s
|++++++++++++++++ | 31% ~46s
|+++++++++++++++++ | 32% ~46s
|+++++++++++++++++ | 33% ~45s
|++++++++++++++++++ | 34% ~44s
|++++++++++++++++++ | 35% ~44s
|+++++++++++++++++++ | 36% ~43s
|+++++++++++++++++++ | 37% ~42s
|++++++++++++++++++++ | 38% ~41s
|++++++++++++++++++++ | 39% ~41s
|+++++++++++++++++++++ | 40% ~40s
|+++++++++++++++++++++ | 41% ~39s
|++++++++++++++++++++++ | 42% ~39s
|++++++++++++++++++++++ | 43% ~38s
|+++++++++++++++++++++++ | 44% ~37s
|+++++++++++++++++++++++ | 45% ~37s
|++++++++++++++++++++++++ | 46% ~36s
|++++++++++++++++++++++++ | 47% ~35s
|+++++++++++++++++++++++++ | 48% ~35s
|+++++++++++++++++++++++++ | 49% ~34s
|++++++++++++++++++++++++++ | 51% ~34s
|++++++++++++++++++++++++++ | 52% ~33s
|+++++++++++++++++++++++++++ | 53% ~32s
|+++++++++++++++++++++++++++ | 54% ~31s
|++++++++++++++++++++++++++++ | 55% ~31s
|++++++++++++++++++++++++++++ | 56% ~30s
|+++++++++++++++++++++++++++++ | 57% ~29s
|+++++++++++++++++++++++++++++ | 58% ~29s
|++++++++++++++++++++++++++++++ | 59% ~28s
|++++++++++++++++++++++++++++++ | 60% ~27s
|+++++++++++++++++++++++++++++++ | 61% ~27s
|+++++++++++++++++++++++++++++++ | 62% ~26s
|++++++++++++++++++++++++++++++++ | 63% ~25s
|++++++++++++++++++++++++++++++++ | 64% ~25s
|+++++++++++++++++++++++++++++++++ | 65% ~24s
|+++++++++++++++++++++++++++++++++ | 66% ~23s
|++++++++++++++++++++++++++++++++++ | 67% ~23s
|++++++++++++++++++++++++++++++++++ | 68% ~22s
|+++++++++++++++++++++++++++++++++++ | 69% ~21s
|+++++++++++++++++++++++++++++++++++ | 70% ~21s
|++++++++++++++++++++++++++++++++++++ | 71% ~20s
|++++++++++++++++++++++++++++++++++++ | 72% ~19s
|+++++++++++++++++++++++++++++++++++++ | 73% ~19s
|+++++++++++++++++++++++++++++++++++++ | 74% ~18s
|++++++++++++++++++++++++++++++++++++++ | 75% ~17s
|++++++++++++++++++++++++++++++++++++++ | 76% ~16s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~16s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~15s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~14s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~14s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~13s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~12s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~12s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~11s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~10s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~10s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~09s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 08s
DT::datatable(MAC.markers)MAC_Volcano_TargetsA = EnhancedVolcano(MAC.markers,
lab = rownames(MAC.markers),
x = "avg_log2FC",
y = "p_val_adj",
selectLab = target_genes_qc,
axisLabSize = 12,
xlab = "average fold-change",
title = "Macrophage markers\n(Macrophage communities vs the rest)",
titleLabSize = 14,
pCutoff = 0.05/(nrow(MAC.markers)), # 20552 genes
FCcutoff = 1.25,
pointSize = 1.5,
labSize = 3.0,
legendLabels =c('NS','avg. fold-change','P',
'P & avg. fold-change'),
legendPosition = "right",
legendLabSize = 10,
legendIconSize = 3.0,
drawConnectors = TRUE,
widthConnectors = 0.2,
colConnectors = "#595A5C",
gridlines.major = FALSE,
gridlines.minor = FALSE)
MAC_Volcano_TargetsAggsave(paste0(PLOT_loc, "/", Today, ".Volcano.MAC.DEG.Targets.pdf"),
plot = MAC_Volcano_TargetsA)Saving 18 x 12 in image
The target results are given below and written to a file.
library(tibble)
MAC.markers <- add_column(MAC.markers, Gene = row.names(MAC.markers), .before = 1)
temp <- MAC.markers[MAC.markers$Gene %in% target_genes_qc,]
DT::datatable(temp)fwrite(temp, file = paste0(OUT_loc, "/", Today, ".MAC.DEG.Targets.txt"),
quote = FALSE,
sep = "\t",
showProgress = FALSE, verbose = FALSE)Comparison between the smooth muscle cell communities (ACTA2+), and all other communities.
SMC.markers <- FindMarkers(object = scRNAseqData,
ident.1 = c("ACTA2+ SMC"),
ident.2 = c("CD68+CASP1+IL1B+SELL MInf",
"CD68+CD1C+ DC",
"CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ MRes",
"CD68+ABCA1+OLR1+TREM2+ FC",
"CD3+ TC I",
"CD3+ TC II",
"CD3+ TC III",
"CD3+ TC IV",
"CD3+ TC V",
"CD3+ TC VI",
"FOXP3+ TC",
"CD34+ EC I",
"CD34+ EC II",
#"ACTA2+ SMC",
"CD3+CD56+ NK I",
"CD3+CD56+ NK II",
"CD68+KIT+ MC",
"CD79+ BCplasma",
"CD79A+ BCmem"))
| | 0 % ~calculating
|+ | 1 % ~01m 29s
|++ | 2 % ~01m 30s
|++ | 3 % ~01m 27s
|+++ | 4 % ~01m 25s
|+++ | 5 % ~01m 22s
|++++ | 6 % ~01m 22s
|++++ | 7 % ~01m 20s
|+++++ | 8 % ~01m 22s
|+++++ | 9 % ~01m 20s
|++++++ | 10% ~01m 21s
|++++++ | 11% ~01m 21s
|+++++++ | 12% ~01m 19s
|+++++++ | 13% ~01m 20s
|++++++++ | 14% ~01m 19s
|++++++++ | 15% ~01m 18s
|+++++++++ | 16% ~01m 18s
|+++++++++ | 17% ~01m 18s
|++++++++++ | 18% ~01m 17s
|++++++++++ | 19% ~01m 15s
|+++++++++++ | 20% ~01m 14s
|+++++++++++ | 21% ~01m 13s
|++++++++++++ | 22% ~01m 11s
|++++++++++++ | 23% ~01m 10s
|+++++++++++++ | 24% ~01m 09s
|+++++++++++++ | 26% ~01m 07s
|++++++++++++++ | 27% ~01m 08s
|++++++++++++++ | 28% ~01m 06s
|+++++++++++++++ | 29% ~01m 05s
|+++++++++++++++ | 30% ~01m 04s
|++++++++++++++++ | 31% ~01m 03s
|++++++++++++++++ | 32% ~01m 02s
|+++++++++++++++++ | 33% ~01m 02s
|+++++++++++++++++ | 34% ~01m 01s
|++++++++++++++++++ | 35% ~60s
|++++++++++++++++++ | 36% ~59s
|+++++++++++++++++++ | 37% ~58s
|+++++++++++++++++++ | 38% ~57s
|++++++++++++++++++++ | 39% ~56s
|++++++++++++++++++++ | 40% ~55s
|+++++++++++++++++++++ | 41% ~54s
|+++++++++++++++++++++ | 42% ~53s
|++++++++++++++++++++++ | 43% ~52s
|++++++++++++++++++++++ | 44% ~51s
|+++++++++++++++++++++++ | 45% ~50s
|+++++++++++++++++++++++ | 46% ~49s
|++++++++++++++++++++++++ | 47% ~48s
|++++++++++++++++++++++++ | 48% ~47s
|+++++++++++++++++++++++++ | 49% ~46s
|+++++++++++++++++++++++++ | 50% ~46s
|++++++++++++++++++++++++++ | 51% ~45s
|+++++++++++++++++++++++++++ | 52% ~43s
|+++++++++++++++++++++++++++ | 53% ~43s
|++++++++++++++++++++++++++++ | 54% ~42s
|++++++++++++++++++++++++++++ | 55% ~41s
|+++++++++++++++++++++++++++++ | 56% ~40s
|+++++++++++++++++++++++++++++ | 57% ~39s
|++++++++++++++++++++++++++++++ | 58% ~38s
|++++++++++++++++++++++++++++++ | 59% ~37s
|+++++++++++++++++++++++++++++++ | 60% ~36s
|+++++++++++++++++++++++++++++++ | 61% ~35s
|++++++++++++++++++++++++++++++++ | 62% ~34s
|++++++++++++++++++++++++++++++++ | 63% ~33s
|+++++++++++++++++++++++++++++++++ | 64% ~32s
|+++++++++++++++++++++++++++++++++ | 65% ~31s
|++++++++++++++++++++++++++++++++++ | 66% ~30s
|++++++++++++++++++++++++++++++++++ | 67% ~29s
|+++++++++++++++++++++++++++++++++++ | 68% ~29s
|+++++++++++++++++++++++++++++++++++ | 69% ~28s
|++++++++++++++++++++++++++++++++++++ | 70% ~27s
|++++++++++++++++++++++++++++++++++++ | 71% ~26s
|+++++++++++++++++++++++++++++++++++++ | 72% ~25s
|+++++++++++++++++++++++++++++++++++++ | 73% ~24s
|++++++++++++++++++++++++++++++++++++++ | 74% ~23s
|++++++++++++++++++++++++++++++++++++++ | 76% ~22s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~21s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~20s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~19s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~18s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~17s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~17s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~16s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~15s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~14s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~13s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~12s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 29s
DT::datatable(SMC.markers)SMC_Volcano_TargetsA = EnhancedVolcano(SMC.markers,
lab = rownames(SMC.markers),
x = "avg_log2FC",
y = "p_val_adj",
selectLab = target_genes_qc,
axisLabSize = 12,
xlab = "average fold-change",
title = "SMC markers\n(SMC communities vs the rest)",
titleLabSize = 14,
pCutoff = 0.05/(nrow(SMC.markers)), # 20552 genes
FCcutoff = 1.25,
pointSize = 1.5,
labSize = 3.0,
legendLabels =c('NS','avg. fold-change','P',
'P & avg. fold-change'),
legendPosition = "right",
legendLabSize = 10,
legendIconSize = 3.0,
drawConnectors = TRUE,
widthConnectors = 0.2,
colConnectors = "#595A5C",
gridlines.major = FALSE,
gridlines.minor = FALSE)
SMC_Volcano_TargetsAggsave(paste0(PLOT_loc, "/", Today, ".Volcano.SMC.DEG.Targets.pdf"),
plot = SMC_Volcano_TargetsA)Saving 18 x 12 in image
The target results are given below and written to a file.
library(tibble)
SMC.markers <- add_column(SMC.markers, Gene = row.names(SMC.markers), .before = 1)
temp <- SMC.markers[SMC.markers$Gene %in% target_genes_qc,]
DT::datatable(temp)fwrite(temp, file = paste0(OUT_loc, "/", Today, ".SMC.DEG.Targets.txt"),
quote = FALSE,
sep = "\t",
showProgress = FALSE, verbose = FALSE)Comparison between the endothelial cell communities (CD34+), and all other communities.
EC.markers <- FindMarkers(object = scRNAseqData,
ident.1 = c("CD34+ EC I",
"CD34+ EC II"),
ident.2 = c("CD68+CASP1+IL1B+SELL MInf",
"CD68+CD1C+ DC",
"CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ MRes",
"CD68+ABCA1+OLR1+TREM2+ FC",
"CD3+ TC I",
"CD3+ TC II",
"CD3+ TC III",
"CD3+ TC IV",
"CD3+ TC V",
"CD3+ TC VI",
"FOXP3+ TC",
# "CD34+ EC I",
# "CD34+ EC II",
"ACTA2+ SMC",
"CD3+CD56+ NK I",
"CD3+CD56+ NK II",
"CD68+KIT+ MC",
"CD79+ BCplasma",
"CD79A+ BCmem"))
| | 0 % ~calculating
|+ | 1 % ~01m 33s
|++ | 2 % ~01m 28s
|++ | 3 % ~01m 21s
|+++ | 4 % ~01m 16s
|+++ | 5 % ~01m 16s
|++++ | 6 % ~01m 16s
|++++ | 7 % ~01m 14s
|+++++ | 8 % ~01m 12s
|+++++ | 9 % ~01m 11s
|++++++ | 10% ~01m 11s
|++++++ | 11% ~01m 10s
|+++++++ | 12% ~01m 09s
|+++++++ | 13% ~01m 08s
|++++++++ | 14% ~01m 08s
|++++++++ | 15% ~01m 06s
|+++++++++ | 16% ~01m 05s
|+++++++++ | 17% ~01m 05s
|++++++++++ | 18% ~01m 04s
|++++++++++ | 19% ~01m 03s
|+++++++++++ | 20% ~01m 02s
|+++++++++++ | 21% ~01m 02s
|++++++++++++ | 22% ~01m 01s
|++++++++++++ | 23% ~60s
|+++++++++++++ | 24% ~59s
|+++++++++++++ | 25% ~58s
|++++++++++++++ | 26% ~58s
|++++++++++++++ | 27% ~57s
|+++++++++++++++ | 28% ~55s
|+++++++++++++++ | 29% ~55s
|++++++++++++++++ | 30% ~54s
|++++++++++++++++ | 31% ~53s
|+++++++++++++++++ | 32% ~52s
|+++++++++++++++++ | 33% ~52s
|++++++++++++++++++ | 34% ~51s
|++++++++++++++++++ | 35% ~50s
|+++++++++++++++++++ | 36% ~49s
|+++++++++++++++++++ | 37% ~48s
|++++++++++++++++++++ | 38% ~48s
|++++++++++++++++++++ | 39% ~47s
|+++++++++++++++++++++ | 40% ~46s
|+++++++++++++++++++++ | 41% ~45s
|++++++++++++++++++++++ | 42% ~45s
|++++++++++++++++++++++ | 43% ~44s
|+++++++++++++++++++++++ | 44% ~43s
|+++++++++++++++++++++++ | 45% ~42s
|++++++++++++++++++++++++ | 46% ~41s
|++++++++++++++++++++++++ | 47% ~41s
|+++++++++++++++++++++++++ | 48% ~40s
|+++++++++++++++++++++++++ | 49% ~39s
|++++++++++++++++++++++++++ | 51% ~38s
|++++++++++++++++++++++++++ | 52% ~37s
|+++++++++++++++++++++++++++ | 53% ~36s
|+++++++++++++++++++++++++++ | 54% ~36s
|++++++++++++++++++++++++++++ | 55% ~35s
|++++++++++++++++++++++++++++ | 56% ~34s
|+++++++++++++++++++++++++++++ | 57% ~33s
|+++++++++++++++++++++++++++++ | 58% ~33s
|++++++++++++++++++++++++++++++ | 59% ~32s
|++++++++++++++++++++++++++++++ | 60% ~31s
|+++++++++++++++++++++++++++++++ | 61% ~30s
|+++++++++++++++++++++++++++++++ | 62% ~29s
|++++++++++++++++++++++++++++++++ | 63% ~29s
|++++++++++++++++++++++++++++++++ | 64% ~28s
|+++++++++++++++++++++++++++++++++ | 65% ~27s
|+++++++++++++++++++++++++++++++++ | 66% ~26s
|++++++++++++++++++++++++++++++++++ | 67% ~26s
|++++++++++++++++++++++++++++++++++ | 68% ~25s
|+++++++++++++++++++++++++++++++++++ | 69% ~24s
|+++++++++++++++++++++++++++++++++++ | 70% ~23s
|++++++++++++++++++++++++++++++++++++ | 71% ~23s
|++++++++++++++++++++++++++++++++++++ | 72% ~22s
|+++++++++++++++++++++++++++++++++++++ | 73% ~21s
|+++++++++++++++++++++++++++++++++++++ | 74% ~20s
|++++++++++++++++++++++++++++++++++++++ | 75% ~19s
|++++++++++++++++++++++++++++++++++++++ | 76% ~19s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~18s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~17s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~16s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~16s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~15s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~14s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~13s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~12s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~12s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~11s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~10s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 15s
DT::datatable(EC.markers)EC_Volcano_TargetsA = EnhancedVolcano(EC.markers,
lab = rownames(EC.markers),
x = "avg_log2FC",
y = "p_val_adj",
selectLab = target_genes_qc,
axisLabSize = 12,
xlab = "average fold-change",
title = "Endothelial cell markers\n(EC communities vs the rest)",
titleLabSize = 14,
pCutoff = 0.05/(nrow(EC.markers)), # 20552 genes
FCcutoff = 1.25,
pointSize = 1.5,
labSize = 3.0,
legendLabels =c('NS','avg. fold-change','P',
'P & avg. fold-change'),
legendPosition = "right",
legendLabSize = 10,
legendIconSize = 3.0,
drawConnectors = TRUE,
widthConnectors = 0.2,
colConnectors = "#595A5C",
gridlines.major = FALSE,
gridlines.minor = FALSE)
EC_Volcano_TargetsAggsave(paste0(PLOT_loc, "/", Today, ".Volcano.EC.DEG.Targets.pdf"),
plot = EC_Volcano_TargetsA)Saving 18 x 12 in image
The target results are given below and written to a file.
library(tibble)
EC.markers <- add_column(EC.markers, Gene = row.names(EC.markers), .before = 1)
temp <- EC.markers[EC.markers$Gene %in% target_genes_qc,]
DT::datatable(temp)fwrite(temp, file = paste0(OUT_loc, "/", Today, ".EC.DEG.Targets.txt"),
quote = FALSE,
sep = "\t",
showProgress = FALSE, verbose = FALSE)Comparison between the T-cell communities (CD3/CD4/CD8+), and all other communities.
TC.markers <- FindMarkers(object = scRNAseqData,
ident.1 = c("CD3+ TC I",
"CD3+ TC II",
"CD3+ TC III",
"CD3+ TC IV",
"CD3+ TC V",
"CD3+ TC VI",
"FOXP3+ TC"),
ident.2 = c("CD68+CASP1+IL1B+SELL MInf",
"CD68+CD1C+ DC",
"CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ MRes",
"CD68+ABCA1+OLR1+TREM2+ FC",
# "CD3+ TC I",
# "CD3+ TC II",
# "CD3+ TC III",
# "CD3+ TC IV",
# "CD3+ TC V",
# "CD3+ TC VI",
# "FOXP3+ TC",
"CD34+ EC I",
"CD34+ EC II",
"ACTA2+ SMC",
"CD3+CD56+ NK I",
"CD3+CD56+ NK II",
"CD68+KIT+ MC",
"CD79+ BCplasma",
"CD79A+ BCmem"))
| | 0 % ~calculating
|+ | 1 % ~54s
|++ | 2 % ~54s
|++ | 3 % ~54s
|+++ | 4 % ~56s
|+++ | 5 % ~54s
|++++ | 6 % ~52s
|++++ | 7 % ~50s
|+++++ | 8 % ~51s
|+++++ | 9 % ~51s
|++++++ | 10% ~50s
|++++++ | 11% ~49s
|+++++++ | 12% ~48s
|+++++++ | 13% ~47s
|++++++++ | 14% ~47s
|++++++++ | 15% ~47s
|+++++++++ | 16% ~46s
|+++++++++ | 17% ~45s
|++++++++++ | 18% ~45s
|++++++++++ | 19% ~45s
|+++++++++++ | 20% ~44s
|+++++++++++ | 21% ~44s
|++++++++++++ | 22% ~43s
|++++++++++++ | 23% ~42s
|+++++++++++++ | 24% ~41s
|+++++++++++++ | 26% ~41s
|++++++++++++++ | 27% ~40s
|++++++++++++++ | 28% ~40s
|+++++++++++++++ | 29% ~39s
|+++++++++++++++ | 30% ~38s
|++++++++++++++++ | 31% ~38s
|++++++++++++++++ | 32% ~37s
|+++++++++++++++++ | 33% ~37s
|+++++++++++++++++ | 34% ~36s
|++++++++++++++++++ | 35% ~36s
|++++++++++++++++++ | 36% ~35s
|+++++++++++++++++++ | 37% ~35s
|+++++++++++++++++++ | 38% ~34s
|++++++++++++++++++++ | 39% ~34s
|++++++++++++++++++++ | 40% ~33s
|+++++++++++++++++++++ | 41% ~32s
|+++++++++++++++++++++ | 42% ~01h 23m 58s
|++++++++++++++++++++++ | 43% ~01h 20m 36s
|++++++++++++++++++++++ | 44% ~01h 17m 23s
|+++++++++++++++++++++++ | 45% ~01h 14m 16s
|+++++++++++++++++++++++ | 46% ~01h 11m 17s
|++++++++++++++++++++++++ | 47% ~01h 08m 26s
|++++++++++++++++++++++++ | 48% ~01h 05m 43s
|+++++++++++++++++++++++++ | 49% ~01h 03m 06s
|+++++++++++++++++++++++++ | 50% ~01h 00m 35s
|++++++++++++++++++++++++++ | 51% ~58m 10s
|+++++++++++++++++++++++++++ | 52% ~55m 52s
|+++++++++++++++++++++++++++ | 53% ~53m 38s
|++++++++++++++++++++++++++++ | 54% ~51m 29s
|++++++++++++++++++++++++++++ | 55% ~49m 25s
|+++++++++++++++++++++++++++++ | 56% ~47m 26s
|+++++++++++++++++++++++++++++ | 57% ~45m 31s
|++++++++++++++++++++++++++++++ | 58% ~43m 39s
|++++++++++++++++++++++++++++++ | 59% ~41m 52s
|+++++++++++++++++++++++++++++++ | 60% ~40m 08s
|+++++++++++++++++++++++++++++++ | 61% ~38m 28s
|++++++++++++++++++++++++++++++++ | 62% ~36m 51s
|++++++++++++++++++++++++++++++++ | 63% ~35m 17s
|+++++++++++++++++++++++++++++++++ | 64% ~33m 45s
|+++++++++++++++++++++++++++++++++ | 65% ~32m 17s
|++++++++++++++++++++++++++++++++++ | 66% ~30m 52s
|++++++++++++++++++++++++++++++++++ | 67% ~29m 29s
|+++++++++++++++++++++++++++++++++++ | 68% ~28m 08s
|+++++++++++++++++++++++++++++++++++ | 69% ~26m 50s
|++++++++++++++++++++++++++++++++++++ | 70% ~25m 34s
|++++++++++++++++++++++++++++++++++++ | 71% ~24m 20s
|+++++++++++++++++++++++++++++++++++++ | 72% ~23m 08s
|+++++++++++++++++++++++++++++++++++++ | 73% ~21m 58s
|++++++++++++++++++++++++++++++++++++++ | 74% ~20m 50s
|++++++++++++++++++++++++++++++++++++++ | 76% ~19m 44s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~18m 40s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~17m 37s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~16m 36s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~15m 37s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~14m 39s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~13m 42s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~12m 47s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~11m 53s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~11m 01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~10m 09s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~09m 19s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~08m 30s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~07m 43s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~06m 56s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~06m 10s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~05m 25s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~04m 42s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~03m 59s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03m 17s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~02m 36s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01m 56s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01m 16s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~38s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01h 01m 04s
DT::datatable(TC.markers)TC_Volcano_TargetsA = EnhancedVolcano(TC.markers,
lab = rownames(TC.markers),
x = "avg_log2FC",
y = "p_val_adj",
selectLab = target_genes_qc,
axisLabSize = 12,
xlab = "average fold-change",
title = "T-cell markers\n(T-cell communities vs the rest)",
titleLabSize = 14,
pCutoff = 0.05/nrow(TC.markers), # 20552 genes
FCcutoff = 1.25,
pointSize = 1.5,
labSize = 3.0,
legendLabels =c('NS','avg. fold-change','P',
'P & avg. fold-change'),
legendPosition = "right",
legendLabSize = 10,
legendIconSize = 3.0,
drawConnectors = TRUE,
widthConnectors = 0.2,
colConnectors = "#595A5C",
gridlines.major = FALSE,
gridlines.minor = FALSE)
TC_Volcano_TargetsAggsave(paste0(PLOT_loc, "/", Today, ".Volcano.TC.DEG.Targets.pdf"),
plot = TC_Volcano_TargetsA)Saving 18 x 12 in image
The target results are given below and written to a file.
library(tibble)
TC.markers <- add_column(TC.markers, Gene = row.names(TC.markers), .before = 1)
temp <- TC.markers[TC.markers$Gene %in% target_genes_qc,]
DT::datatable(temp)fwrite(temp, file = paste0(OUT_loc, "/", Today, ".TC.DEG.Targets.txt"),
quote = FALSE,
sep = "\t",
showProgress = FALSE, verbose = FALSE)Comparison between the B-cell communities (CD79A+), and all other communities.
BC.markers <- FindMarkers(object = scRNAseqData,
ident.1 = c("CD79+ BCplasma",
"CD79A+ BCmem"),
ident.2 = c("CD68+CASP1+IL1B+SELL MInf",
"CD68+CD1C+ DC",
"CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ MRes",
"CD68+ABCA1+OLR1+TREM2+ FC",
"CD3+ TC I",
"CD3+ TC II",
"CD3+ TC III",
"CD3+ TC IV",
"CD3+ TC V",
"CD3+ TC VI",
"FOXP3+ TC",
"CD34+ EC I",
"CD34+ EC II",
"ACTA2+ SMC",
"CD3+CD56+ NK I",
"CD3+CD56+ NK II",
"CD68+KIT+ MC"
# "CD79+ BCplasma",
# "CD79A+ BCmem"
))
| | 0 % ~calculating
|+ | 1 % ~54s
|+ | 2 % ~48s
|++ | 3 % ~46s
|++ | 4 % ~44s
|+++ | 5 % ~43s
|+++ | 6 % ~45s
|++++ | 7 % ~44s
|++++ | 8 % ~43s
|+++++ | 9 % ~42s
|+++++ | 10% ~41s
|++++++ | 11% ~40s
|++++++ | 12% ~40s
|+++++++ | 13% ~39s
|+++++++ | 14% ~39s
|++++++++ | 15% ~38s
|++++++++ | 16% ~38s
|+++++++++ | 17% ~37s
|+++++++++ | 18% ~37s
|++++++++++ | 19% ~36s
|++++++++++ | 20% ~36s
|+++++++++++ | 21% ~35s
|+++++++++++ | 22% ~34s
|++++++++++++ | 23% ~42s
|++++++++++++ | 24% ~41s
|+++++++++++++ | 25% ~40s
|+++++++++++++ | 26% ~39s
|++++++++++++++ | 27% ~38s
|++++++++++++++ | 28% ~38s
|+++++++++++++++ | 29% ~37s
|+++++++++++++++ | 30% ~36s
|++++++++++++++++ | 31% ~35s
|++++++++++++++++ | 32% ~35s
|+++++++++++++++++ | 33% ~34s
|+++++++++++++++++ | 34% ~33s
|++++++++++++++++++ | 35% ~33s
|++++++++++++++++++ | 36% ~32s
|+++++++++++++++++++ | 37% ~31s
|+++++++++++++++++++ | 38% ~31s
|++++++++++++++++++++ | 39% ~30s
|++++++++++++++++++++ | 40% ~30s
|+++++++++++++++++++++ | 41% ~29s
|+++++++++++++++++++++ | 42% ~28s
|++++++++++++++++++++++ | 43% ~28s
|++++++++++++++++++++++ | 44% ~27s
|+++++++++++++++++++++++ | 45% ~25m 17s
|+++++++++++++++++++++++ | 46% ~24m 19s
|++++++++++++++++++++++++ | 47% ~23m 23s
|++++++++++++++++++++++++ | 48% ~22m 29s
|+++++++++++++++++++++++++ | 49% ~21m 37s
|+++++++++++++++++++++++++ | 50% ~20m 47s
|++++++++++++++++++++++++++ | 51% ~19m 58s
|++++++++++++++++++++++++++ | 52% ~19m 12s
|+++++++++++++++++++++++++++ | 53% ~18m 27s
|+++++++++++++++++++++++++++ | 54% ~17m 44s
|++++++++++++++++++++++++++++ | 55% ~17m 02s
|++++++++++++++++++++++++++++ | 56% ~16m 22s
|+++++++++++++++++++++++++++++ | 57% ~15m 44s
|+++++++++++++++++++++++++++++ | 58% ~15m 06s
|++++++++++++++++++++++++++++++ | 59% ~14m 30s
|++++++++++++++++++++++++++++++ | 60% ~13m 55s
|+++++++++++++++++++++++++++++++ | 61% ~13m 21s
|+++++++++++++++++++++++++++++++ | 62% ~12m 48s
|++++++++++++++++++++++++++++++++ | 63% ~12m 16s
|++++++++++++++++++++++++++++++++ | 64% ~11m 46s
|+++++++++++++++++++++++++++++++++ | 65% ~11m 16s
|+++++++++++++++++++++++++++++++++ | 66% ~10m 47s
|++++++++++++++++++++++++++++++++++ | 67% ~10m 19s
|++++++++++++++++++++++++++++++++++ | 68% ~09m 51s
|+++++++++++++++++++++++++++++++++++ | 69% ~09m 25s
|+++++++++++++++++++++++++++++++++++ | 70% ~08m 59s
|++++++++++++++++++++++++++++++++++++ | 71% ~08m 34s
|++++++++++++++++++++++++++++++++++++ | 72% ~08m 10s
|+++++++++++++++++++++++++++++++++++++ | 73% ~07m 46s
|+++++++++++++++++++++++++++++++++++++ | 74% ~07m 23s
|++++++++++++++++++++++++++++++++++++++ | 75% ~07m 00s
|++++++++++++++++++++++++++++++++++++++ | 76% ~06m 38s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~06m 17s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~05m 56s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~05m 36s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~05m 16s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~04m 56s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~04m 37s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~04m 19s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~04m 01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03m 43s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~03m 26s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~03m 09s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02m 53s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02m 37s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02m 21s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02m 06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01m 50s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01m 36s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01m 21s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01m 07s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~53s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~39s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~26s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~13s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=21m 14s
DT::datatable(BC.markers)BC_Volcano_TargetsA = EnhancedVolcano(BC.markers,
lab = rownames(BC.markers),
x = "avg_log2FC",
y = "p_val_adj",
selectLab = target_genes_qc,
axisLabSize = 12,
xlab = "average fold-change",
title = "B-cell markers\n(B-cell communities vs the rest)",
titleLabSize = 14,
pCutoff = 0.05/nrow(BC.markers), # 20552 genes
FCcutoff = 1.25,
pointSize = 1.5,
labSize = 3.0,
legendLabels =c('NS','avg. fold-change','P',
'P & avg. fold-change'),
legendPosition = "right",
legendLabSize = 10,
legendIconSize = 3.0,
drawConnectors = TRUE,
widthConnectors = 0.2,
colConnectors = "#595A5C",
gridlines.major = FALSE,
gridlines.minor = FALSE)
BC_Volcano_TargetsAggsave(paste0(PLOT_loc, "/", Today, ".Volcano.BC.DEG.Targets.pdf"),
plot = BC_Volcano_TargetsA)Saving 18 x 12 in image
The target results are given below and written to a file.
library(tibble)
BC.markers <- add_column(BC.markers, Gene = row.names(BC.markers), .before = 1)
temp <- BC.markers[BC.markers$Gene %in% target_genes_qc,]
DT::datatable(temp)fwrite(temp, file = paste0(OUT_loc, "/", Today, ".BC.DEG.Targets.txt"),
quote = FALSE,
sep = "\t",
showProgress = FALSE, verbose = FALSE)Comparison between the mast cell communities (KIT+), and all other communities.
MC.markers <- FindMarkers(object = scRNAseqData,
ident.1 = c("CD68+KIT+ MC"),
ident.2 = c("CD68+CASP1+IL1B+SELL MInf",
"CD68+CD1C+ DC",
"CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ MRes",
"CD68+ABCA1+OLR1+TREM2+ FC",
"CD3+ TC I",
"CD3+ TC II",
"CD3+ TC III",
"CD3+ TC IV",
"CD3+ TC V",
"CD3+ TC VI",
"FOXP3+ TC",
"CD34+ EC I",
"CD34+ EC II",
"ACTA2+ SMC",
"CD3+CD56+ NK I",
"CD3+CD56+ NK II",
# "CD68+KIT+ MC",
"CD79+ BCplasma",
"CD79A+ BCmem"))
| | 0 % ~calculating
|+ | 1 % ~01m 12s
|+ | 2 % ~01m 38s
|++ | 3 % ~01m 31s
|++ | 4 % ~01m 24s
|+++ | 5 % ~01m 21s
|+++ | 6 % ~01m 20s
|++++ | 7 % ~01m 19s
|++++ | 8 % ~01m 16s
|+++++ | 9 % ~01m 14s
|+++++ | 10% ~01m 13s
|++++++ | 11% ~01m 13s
|++++++ | 12% ~01m 11s
|+++++++ | 13% ~01m 10s
|+++++++ | 14% ~01m 09s
|++++++++ | 15% ~01m 09s
|++++++++ | 16% ~01m 07s
|+++++++++ | 17% ~01m 06s
|+++++++++ | 18% ~01m 06s
|++++++++++ | 19% ~01m 05s
|++++++++++ | 20% ~01m 04s
|+++++++++++ | 21% ~01m 03s
|+++++++++++ | 22% ~01m 02s
|++++++++++++ | 23% ~01m 02s
|++++++++++++ | 24% ~01m 00s
|+++++++++++++ | 25% ~01m 00s
|+++++++++++++ | 26% ~59s
|++++++++++++++ | 27% ~59s
|++++++++++++++ | 28% ~57s
|+++++++++++++++ | 29% ~57s
|+++++++++++++++ | 30% ~56s
|++++++++++++++++ | 31% ~55s
|++++++++++++++++ | 32% ~54s
|+++++++++++++++++ | 33% ~53s
|+++++++++++++++++ | 34% ~53s
|++++++++++++++++++ | 35% ~52s
|++++++++++++++++++ | 36% ~51s
|+++++++++++++++++++ | 37% ~50s
|+++++++++++++++++++ | 38% ~49s
|++++++++++++++++++++ | 39% ~49s
|++++++++++++++++++++ | 40% ~48s
|+++++++++++++++++++++ | 41% ~47s
|+++++++++++++++++++++ | 42% ~46s
|++++++++++++++++++++++ | 43% ~45s
|++++++++++++++++++++++ | 44% ~44s
|+++++++++++++++++++++++ | 45% ~43s
|+++++++++++++++++++++++ | 46% ~43s
|++++++++++++++++++++++++ | 47% ~42s
|++++++++++++++++++++++++ | 48% ~41s
|+++++++++++++++++++++++++ | 49% ~40s
|+++++++++++++++++++++++++ | 50% ~40s
|++++++++++++++++++++++++++ | 51% ~39s
|++++++++++++++++++++++++++ | 52% ~38s
|+++++++++++++++++++++++++++ | 53% ~37s
|+++++++++++++++++++++++++++ | 54% ~36s
|++++++++++++++++++++++++++++ | 55% ~35s
|++++++++++++++++++++++++++++ | 56% ~35s
|+++++++++++++++++++++++++++++ | 57% ~34s
|+++++++++++++++++++++++++++++ | 58% ~33s
|++++++++++++++++++++++++++++++ | 59% ~32s
|++++++++++++++++++++++++++++++ | 60% ~31s
|+++++++++++++++++++++++++++++++ | 61% ~31s
|+++++++++++++++++++++++++++++++ | 62% ~30s
|++++++++++++++++++++++++++++++++ | 63% ~29s
|++++++++++++++++++++++++++++++++ | 64% ~28s
|+++++++++++++++++++++++++++++++++ | 65% ~27s
|+++++++++++++++++++++++++++++++++ | 66% ~27s
|++++++++++++++++++++++++++++++++++ | 67% ~26s
|++++++++++++++++++++++++++++++++++ | 68% ~25s
|+++++++++++++++++++++++++++++++++++ | 69% ~24s
|+++++++++++++++++++++++++++++++++++ | 70% ~23s
|++++++++++++++++++++++++++++++++++++ | 71% ~23s
|++++++++++++++++++++++++++++++++++++ | 72% ~22s
|+++++++++++++++++++++++++++++++++++++ | 73% ~21s
|+++++++++++++++++++++++++++++++++++++ | 74% ~20s
|++++++++++++++++++++++++++++++++++++++ | 75% ~20s
|++++++++++++++++++++++++++++++++++++++ | 76% ~19s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~18s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~17s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~16s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~16s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~15s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~14s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~13s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~13s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~12s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~11s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~10s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 17s
DT::datatable(MC.markers)MC_Volcano_TargetsA = EnhancedVolcano(MC.markers,
lab = rownames(MC.markers),
x = "avg_log2FC",
y = "p_val_adj",
selectLab = target_genes_qc,
axisLabSize = 12,
xlab = "average fold-change",
title = "Mast cell markers\n(Mast cell communities vs the rest)",
titleLabSize = 14,
pCutoff = 0.05/nrow(MC.markers), # 20552 genes
FCcutoff = 1.25,
pointSize = 1.5,
labSize = 3.0,
legendLabels =c('NS','avg. fold-change','P',
'P & avg. fold-change'),
legendPosition = "right",
legendLabSize = 10,
legendIconSize = 3.0,
drawConnectors = TRUE,
widthConnectors = 0.2,
colConnectors = "#595A5C",
gridlines.major = FALSE,
gridlines.minor = FALSE)
MC_Volcano_TargetsAggsave(paste0(PLOT_loc, "/", Today, ".Volcano.MC.DEG.Targets.pdf"),
plot = MC_Volcano_TargetsA)Saving 18 x 12 in image
The target results are given below and written to a file.
library(tibble)
MC.markers <- add_column(MC.markers, Gene = row.names(MC.markers), .before = 1)
temp <- MC.markers[MC.markers$Gene %in% target_genes_qc,]
DT::datatable(temp)fwrite(temp, file = paste0(OUT_loc, "/", Today, ".MC.DEG.Targets.txt"),
quote = FALSE,
sep = "\t",
showProgress = FALSE, verbose = FALSE)Comparison between the natural killer cell communities (NCAM1+), and all other communities.
NK.markers <- FindMarkers(object = scRNAseqData,
ident.1 = c("CD3+CD56+ NK I",
"CD3+CD56+ NK II"),
ident.2 = c("CD68+CASP1+IL1B+SELL MInf",
"CD68+CD1C+ DC",
"CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ MRes",
"CD68+ABCA1+OLR1+TREM2+ FC",
"CD3+ TC I",
"CD3+ TC II",
"CD3+ TC III",
"CD3+ TC IV",
"CD3+ TC V",
"CD3+ TC VI",
"FOXP3+ TC",
"CD34+ EC I",
"CD34+ EC II",
"ACTA2+ SMC",
# "CD3+CD56+ NK I",
# "CD3+CD56+ NK II",
"CD68+KIT+ MC",
"CD79+ BCplasma",
"CD79A+ BCmem"))
| | 0 % ~calculating
|+ | 1 % ~28s
|++ | 2 % ~26s
|++ | 3 % ~28s
|+++ | 4 % ~29s
|+++ | 5 % ~29s
|++++ | 6 % ~29s
|++++ | 7 % ~29s
|+++++ | 8 % ~28s
|+++++ | 9 % ~30s
|++++++ | 10% ~29s
|++++++ | 11% ~28s
|+++++++ | 12% ~27s
|+++++++ | 13% ~27s
|++++++++ | 14% ~27s
|++++++++ | 15% ~26s
|+++++++++ | 16% ~26s
|+++++++++ | 18% ~25s
|++++++++++ | 19% ~25s
|++++++++++ | 20% ~25s
|+++++++++++ | 21% ~24s
|+++++++++++ | 22% ~24s
|++++++++++++ | 23% ~23s
|++++++++++++ | 24% ~23s
|+++++++++++++ | 25% ~23s
|+++++++++++++ | 26% ~22s
|++++++++++++++ | 27% ~22s
|++++++++++++++ | 28% ~22s
|+++++++++++++++ | 29% ~21s
|+++++++++++++++ | 30% ~21s
|++++++++++++++++ | 31% ~21s
|++++++++++++++++ | 32% ~20s
|+++++++++++++++++ | 33% ~20s
|++++++++++++++++++ | 34% ~20s
|++++++++++++++++++ | 35% ~19s
|+++++++++++++++++++ | 36% ~19s
|+++++++++++++++++++ | 37% ~19s
|++++++++++++++++++++ | 38% ~19s
|++++++++++++++++++++ | 39% ~18s
|+++++++++++++++++++++ | 40% ~18s
|+++++++++++++++++++++ | 41% ~18s
|++++++++++++++++++++++ | 42% ~17s
|++++++++++++++++++++++ | 43% ~17s
|+++++++++++++++++++++++ | 44% ~17s
|+++++++++++++++++++++++ | 45% ~16s
|++++++++++++++++++++++++ | 46% ~16s
|++++++++++++++++++++++++ | 47% ~16s
|+++++++++++++++++++++++++ | 48% ~15s
|+++++++++++++++++++++++++ | 49% ~15s
|++++++++++++++++++++++++++ | 51% ~15s
|++++++++++++++++++++++++++ | 52% ~15s
|+++++++++++++++++++++++++++ | 53% ~14s
|+++++++++++++++++++++++++++ | 54% ~14s
|++++++++++++++++++++++++++++ | 55% ~13s
|++++++++++++++++++++++++++++ | 56% ~13s
|+++++++++++++++++++++++++++++ | 57% ~13s
|+++++++++++++++++++++++++++++ | 58% ~13s
|++++++++++++++++++++++++++++++ | 59% ~12s
|++++++++++++++++++++++++++++++ | 60% ~12s
|+++++++++++++++++++++++++++++++ | 61% ~12s
|+++++++++++++++++++++++++++++++ | 62% ~12s
|++++++++++++++++++++++++++++++++ | 63% ~11s
|++++++++++++++++++++++++++++++++ | 64% ~11s
|+++++++++++++++++++++++++++++++++ | 65% ~11s
|+++++++++++++++++++++++++++++++++ | 66% ~11s
|++++++++++++++++++++++++++++++++++ | 67% ~11s
|+++++++++++++++++++++++++++++++++++ | 68% ~10s
|+++++++++++++++++++++++++++++++++++ | 69% ~10s
|++++++++++++++++++++++++++++++++++++ | 70% ~10s
|++++++++++++++++++++++++++++++++++++ | 71% ~09s
|+++++++++++++++++++++++++++++++++++++ | 72% ~09s
|+++++++++++++++++++++++++++++++++++++ | 73% ~09s
|++++++++++++++++++++++++++++++++++++++ | 74% ~08s
|++++++++++++++++++++++++++++++++++++++ | 75% ~08s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~08s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~07s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~07s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~07s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~06s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~06s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~05s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=31s
DT::datatable(NK.markers)NK_Volcano_TargetsA = EnhancedVolcano(NK.markers,
lab = rownames(NK.markers),
x = "avg_log2FC",
y = "p_val_adj",
selectLab = target_genes_qc,
axisLabSize = 12,
xlab = "average fold-change",
title = "NK markers\n(NK-cell communities vs the rest)",
titleLabSize = 14,
pCutoff = 0.05/nrow(NK.markers), # 20552 genes
FCcutoff = 1.25,
pointSize = 1.5,
labSize = 3.0,
legendLabels =c('NS','avg. fold-change','P',
'P & avg. fold-change'),
legendPosition = "right",
legendLabSize = 10,
legendIconSize = 3.0,
drawConnectors = TRUE,
widthConnectors = 0.2,
colConnectors = "#595A5C",
gridlines.major = FALSE,
gridlines.minor = FALSE)
NK_Volcano_TargetsAggsave(paste0(PLOT_loc, "/", Today, ".Volcano.NK.DEG.Targets.pdf"),
plot = NK_Volcano_TargetsA)Saving 18 x 12 in image
The target results are given below and written to a file.
library(tibble)
NK.markers <- add_column(NK.markers, Gene = row.names(NK.markers), .before = 1)
temp <- NK.markers[NK.markers$Gene %in% target_genes_qc,]
DT::datatable(temp)fwrite(temp, file = paste0(OUT_loc, "/", Today, ".NK.DEG.Targets.txt"),
quote = FALSE,
sep = "\t",
showProgress = FALSE, verbose = FALSE)List of samples to be included based on informed consent (see above).
samples_of_interest <- unlist(scRNAseqDataMetaAE.all$Patient)scRNAseqDataCEA39 <- subset(scRNAseqData, subset = Patient %in% samples_of_interest)variables_of_interest <- c("Hospital", "ORyear", "Artery_summary",
"Age", "Gender",
"TC_final", "LDL_final", "HDL_final", "TG_final",
"systolic", "diastoli", "GFR_MDRD", "BMI",
"KDOQI", "BMI_WHO",
"SmokerStatus", "AlcoholUse",
"DiabetesStatus",
"Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
"Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
"Stroke_Dx",
"sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
"Symptoms.Update2G", "Symptoms.Update3G", "indexsymptoms_latest_4g",
"restenos", "stenose",
"CAD_history", "PAOD", "Peripheral.interv",
"EP_composite", "EP_composite_time", "EP_major", "EP_major_time")
temp <- subset(scRNAseqDataMetaAE.all, select = c("Patient", variables_of_interest))
# str(temp)scRNAseqDataCEA39@meta.data <- merge(scRNAseqDataCEA39@meta.data, temp, by.x = "Patient", by.y = "Patient")
scRNAseqDataCEA39@meta.data <- dplyr::rename(scRNAseqDataCEA39@meta.data, "STUDY_NUMBER" = "Patient")
# str(scRNAseqDataCEA39@meta.data)temp2 <- as_tibble(subset(scRNAseqDataCEA39@meta.data, select = c("STUDY_NUMBER", "orig.ident", "nCount_RNA", "nFeature_RNA",
"Plate", "Batch", "C.H", "Type", "percent.mt",
"nCount_SCT", "nFeature_SCT", "seurat_clusters")))
# fwrite(temp2,
# file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.samplelist.after_qc.IC_commercial.csv"),
# sep = ",", row.names = FALSE, col.names = TRUE,
# showProgress = TRUE)
# rm(temp2)
#
# temp <- dplyr::rename(temp, "STUDY_NUMBER" = "Patient")
# fwrite(temp,
# file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.clinicaldata.after_qc.IC_commercial.csv"),
# sep = ",", row.names = FALSE, col.names = TRUE,
# showProgress = TRUE)
# rm(temp)
#
# saveRDS(scRNAseqDataCEA39, file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.Seurat.after_qc.IC_commercial.RDS"))
fwrite(temp2,
file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.samplelist.after_qc.IC_academic.csv"),
sep = ",", row.names = FALSE, col.names = TRUE,
showProgress = TRUE)
rm(temp2)
temp <- dplyr::rename(temp, "STUDY_NUMBER" = "Patient")
fwrite(temp,
file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.clinicaldata.after_qc.IC_academic.csv"),
sep = ",", row.names = FALSE, col.names = TRUE,
showProgress = TRUE)
rm(temp)
saveRDS(scRNAseqDataCEA39, file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.Seurat.after_qc.IC_academic.RDS"))Version: v1.0.1
Last update: 2022-03-19
Written by: Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
Description: Script to load single-cell RNA sequencing (scRNAseq) data, and perform quality control (QC), and initial mapping to cells.
Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).
**MoSCoW To-Do List**
The things we Must, Should, Could, and Would have given the time we have.
_M_
_S_
_C_
_W_
**Changes log**
* v1.0.1 Update to main AEDB (there is an error in the Age-variable in the new version). Fewer patients in scRNAseq (32 vs 39 with the newer dataset).
* v1.0.0 Initial version.
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Monterey 12.2.1
Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats4 grid tools stats graphics grDevices utils datasets methods base
other attached packages:
[1] RColorBrewer_1.1-2 SeuratObject_4.0.4 Seurat_4.0.6
[4] PerformanceAnalytics_2.0.4 xts_0.12.1 zoo_1.8-9
[7] Hmisc_4.6-0 Formula_1.2-4 lattice_0.20-45
[10] survminer_0.4.9 survival_3.3-1 MASS_7.3-54
[13] ggcorrplot_0.1.3.999 GGally_2.1.2 annotables_0.1.91
[16] EnhancedVolcano_1.12.0 ggrepel_0.9.1 AnnotationFilter_1.18.0
[19] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2 mygene_1.30.0 org.Hs.eg.db_3.14.0
[22] DESeq2_1.34.0 SummarizedExperiment_1.24.0 MatrixGenerics_1.6.0
[25] matrixStats_0.61.0 GenomicFeatures_1.46.3 AnnotationDbi_1.56.2
[28] Biobase_2.54.0 GenomicRanges_1.46.1 GenomeInfoDb_1.30.0
[31] IRanges_2.28.0 S4Vectors_0.32.3 BiocGenerics_0.40.0
[34] patchwork_1.1.0.9000 labelled_2.9.0 openxlsx_4.2.5
[37] sjPlot_2.8.10 UpSetR_1.4.0 ggpubr_0.4.0
[40] forestplot_2.0.1 checkmate_2.0.0 magrittr_2.0.2
[43] pheatmap_1.0.12 devtools_2.4.3 usethis_2.1.5
[46] BlandAltmanLeh_0.3.1 tableone_0.13.0 haven_2.4.3
[49] eeptools_1.2.4 DT_0.20 knitr_1.37
[52] forcats_0.5.1 stringr_1.4.0 purrr_0.3.4
[55] tibble_3.1.6 ggplot2_3.3.5 tidyverse_1.3.1
[58] data.table_1.14.2 naniar_0.6.1 tidyr_1.1.4
[61] dplyr_1.0.7 optparse_1.7.1 readr_2.1.1
[64] pander_0.6.4 rmarkdown_2.11 worcs_0.1.9.1
loaded via a namespace (and not attached):
[1] mitools_2.4 pbapply_1.5-0 vctrs_0.3.8 mgcv_1.8-39 blob_1.2.2 spatstat.data_2.1-2
[7] later_1.3.0 nloptr_1.2.2.3 DBI_1.1.2 uwot_0.1.11 rappdirs_0.3.3 gsubfn_0.7
[13] jpeg_0.1-9 zlibbioc_1.40.0 sjmisc_2.8.9 htmlwidgets_1.5.4 mvtnorm_1.1-3 future_1.23.0
[19] leiden_0.3.9 parallel_4.1.2 irlba_2.3.5 markdown_1.1 Rcpp_1.0.8.3 KernSmooth_2.23-20
[25] promises_1.2.0.1 limma_3.50.0 DelayedArray_0.20.0 ggeffects_1.1.1 pkgload_1.2.4 fs_1.5.2
[31] textshaping_0.3.6 ranger_0.13.1 digest_0.6.29 png_0.1-7 sctransform_0.3.2 cowplot_1.1.1
[37] pkgconfig_2.0.3 ggbeeswarm_0.6.0 estimability_1.3 minqa_1.2.4 reticulate_1.22 beeswarm_0.4.0
[43] xfun_0.29 bslib_0.3.1 tidyselect_1.1.1 performance_0.8.0 reshape2_1.4.4 ica_1.0-2
[49] viridisLite_0.4.0 rtracklayer_1.54.0 pkgbuild_1.3.1 rlang_1.0.2 jquerylib_0.1.4 glue_1.6.2
[55] ensembldb_2.18.2 modelr_0.1.8 emmeans_1.7.2 ggsignif_0.6.3 bayestestR_0.11.5 labeling_0.4.2
[61] maptools_1.1-2 httpuv_1.6.5 class_7.3-20 Rttf2pt1_1.3.9 TH.data_1.1-0 annotate_1.72.0
[67] jsonlite_1.7.2 XVector_0.34.0 bit_4.0.4 mime_0.12 systemfonts_1.0.3 gridExtra_2.3
[73] Rsamtools_2.10.0 stringi_1.7.6 insight_0.16.0 processx_3.5.2 spatstat.sparse_2.1-0 scattermore_0.7
[79] survey_4.1-1 quadprog_1.5-8 bitops_1.0-7 cli_3.2.0 sqldf_0.4-11 maps_3.4.0
[85] RSQLite_2.2.9 prereg_0.5.0 rticles_0.22 rsconnect_0.8.25 rstudioapi_0.13 GenomicAlignments_1.30.0
[91] nlme_3.1-155 locfit_1.5-9.4 listenv_0.8.0 miniUI_0.1.1.1 survMisc_0.5.5 dbplyr_2.1.1
[97] sessioninfo_1.2.2 readxl_1.3.1 lifecycle_1.0.1 munsell_0.5.0 cellranger_1.1.0 ggsci_2.9
[103] codetools_0.2-18 coda_0.19-4 vipor_0.4.5 lmtest_0.9-39 sys_3.4 htmlTable_2.4.0
[109] proto_1.0.0 xtable_1.8-4 ROCR_1.0-11 abind_1.4-5 farver_2.1.0 parallelly_1.30.0
[115] km.ci_0.5-2 credentials_1.3.2 RANN_2.6.1 askpass_1.1 visdat_0.5.3 BiocIO_1.4.0
[121] sjstats_0.18.1 goftest_1.2-3 RcppAnnoy_0.0.19 cluster_2.1.2 future.apply_1.8.1 extrafontdb_1.0
[127] Matrix_1.4-0 ellipsis_0.3.2 prettyunits_1.1.1 lubridate_1.8.0 ggridges_0.5.3 reprex_2.0.1
[133] igraph_1.2.11 sjlabelled_1.1.8 remotes_2.4.2 parameters_0.17.0 spatstat.utils_2.3-0 testthat_3.1.1
[139] getopt_1.20.3 htmltools_0.5.2 BiocFileCache_2.2.0 yaml_2.2.1 utf8_1.2.2 plotly_4.10.0
[145] XML_3.99-0.8 e1071_1.7-9 foreign_0.8-82 withr_2.5.0 fitdistrplus_1.1-6 BiocParallel_1.28.3
[151] bit64_4.0.5 effectsize_0.6.0.1 multcomp_1.4-18 ProtGenerics_1.26.0 spatstat.core_2.3-2 Biostrings_2.62.0
[157] ragg_1.2.1 memoise_2.0.1 evaluate_0.14 geneplotter_1.72.0 tzdb_0.2.0 extrafont_0.17
[163] callr_3.7.0 ps_1.6.0 curl_4.3.2 fansi_1.0.2 tensor_1.5 cachem_1.0.6
[169] desc_1.4.0 deldir_1.0-6 proj4_1.0-10.1 rjson_0.2.21 rstatix_0.7.0 rprojroot_2.0.2
[175] sass_0.4.0 sandwich_3.0-1 RCurl_1.98-1.5 proxy_0.4-26 car_3.0-12 xml2_1.3.3
[181] httr_1.4.2 assertthat_0.2.1 boot_1.3-28 globals_0.14.0 R6_2.5.1 nnet_7.3-17
[187] progress_1.2.2 genefilter_1.76.0 KEGGREST_1.34.0 ggrastr_1.0.1 splines_4.1.2 carData_3.0-5
[193] colorspace_2.0-3 generics_0.1.1 base64enc_0.1-3 chron_2.3-56 gridtext_0.1.4 gert_1.5.0
[199] pillar_1.7.0 ggalt_0.4.0 sp_1.4-6 GenomeInfoDbData_1.2.7 plyr_1.8.6 gtable_0.3.0
[205] rvest_1.0.2 zip_2.2.0 restfulr_0.0.13 latticeExtra_0.6-29 biomaRt_2.50.2 fastmap_1.1.0
[211] Cairo_1.5-14 crosstalk_1.2.0 datawizard_0.3.0 vcd_1.4-9 broom_0.7.11 openssl_1.4.6
[217] scales_1.1.1 arm_1.12-2 filelock_1.0.2 backports_1.4.1 lme4_1.1-27.1 hms_1.1.1
[223] Rtsne_0.15 shiny_1.7.1 KMsurv_0.1-5 ash_1.0-15 polyclip_1.10-0 lazyeval_0.2.2
[229] crayon_1.5.0 reshape_0.8.8 rpart_4.1.16 spatstat.geom_2.3-1 compiler_4.1.2 ggtext_0.1.1
rm(backup.scRNAseqData)
rm(scRNAseqData, scRNAseqDataCEA39)
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".AESCRNA.results.RData"))| © 1979-2022 Sander W. van der Laan | s.w.vanderlaan[at]gmail.com swvanderlaan.github.io. |